Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region

• Published in: PloS one Vol. 10; no. 8; p. e0133908
• Main Authors: Tian, Jing, Varga, Boglárka, Somfai, Gábor Márk, Lee, Wen-Hsiang, Smiddy, William E., Cabrera DeBuc, Delia
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.08.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Boundaries; Computer Graphics; Dependence; Diagnostic Imaging - methods; Humans; Image analysis; Image processing; Image Processing, Computer-Assisted - methods; Image resolution; Image segmentation; Imaging, Three-Dimensional - methods; Macula Lutea - pathology; Macular degeneration; Masking; Medical imaging; Optical Coherence Tomography; Optics; Pattern recognition; Quantitative analysis; Real time; Reproducibility of Results; Retina; Retina - pathology; Segmentation; Shortest-path problems; Software; Tomography; Tomography, Optical Coherence - methods; Tomography, X-Ray Computed - methods; Iowa; Hungary; Florida; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0133908

Optical coherence tomography (OCT) is a high speed, high resolution and non-invasive imaging modality that enables the capturing of the 3D structure of the retina. The fast and automatic analysis of 3D volume OCT data is crucial taking into account the increased amount of patient-specific 3D imaging data. In this work, we have developed an automatic algorithm, OCTRIMA 3D (OCT Retinal IMage Analysis 3D), that could segment OCT volume data in the macular region fast and accurately. The proposed method is implemented using the shortest-path based graph search, which detects the retinal boundaries by searching the shortest-path between two end nodes using Dijkstra's algorithm. Additional techniques, such as inter-frame flattening, inter-frame search region refinement, masking and biasing were introduced to exploit the spatial dependency between adjacent frames for the reduction of the processing time. Our segmentation algorithm was evaluated by comparing with the manual labelings and three state of the art graph-based segmentation methods. The processing time for the whole OCT volume of 496×644×51 voxels (captured by Spectralis SD-OCT) was 26.15 seconds which is at least a 2-8-fold increase in speed compared to other, similar reference algorithms used in the comparisons. The average unsigned error was about 1 pixel (∼ 4 microns), which was also lower compared to the reference algorithms. We believe that OCTRIMA 3D is a leap forward towards achieving reliable, real-time analysis of 3D OCT retinal data.